Economic and financial modeling and planning is commonly used to estimate or predict the performance and outcome of real systems, given specific sets of input data of interest. An economic-based system will have many variables and influences which determine its behavior. A model is a mathematical expression or representation which predicts the outcome or behavior of the system under a variety of conditions. In one sense, it is relatively easy to review historical data, understand its past performance, and state with relative certainty that the system's past behavior was indeed driven by the historical data. A much more difficult task is to generate a mathematical model of the system which predicts how the system will behave with different sets of data and assumptions.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a mathematical expression, as driven by a given set of input data and assumptions. The input data are processed through the mathematical expression representing either the expected or current behavior of the real system. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve a best fit of the expected behavior of the system to other sets of data. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty.
Economic modeling has many uses and applications. One area in which modeling has been applied is in the retail environment. Grocery stores, general merchandise stores, specialty shops, and other retail outlets face stiff competition for limited customers and business. Most if not all retail stores expend great effort to maximize sales, volume, revenue, and/or profit. Economic modeling can be a very effective tool in helping the store owners and managers achieve these goals.
Economic modeling typically requires large amounts of data. In the retail environment, the data is collected at the completion of the transaction, usually during the check-out process. The transactional log (T-LOG) data contains information about the items purchased, time and date of purchase, store, price, promotions, customer attributes, and so on. The T-LOG data is stored in a database for use by the retailer in generating and using the models.
To prepare a model of sales or demand of any particular product at a particular store, the T-LOG data is first aggregated over one or more variable. The aggregated T-LOG data is analyzed and a series of parameters are generated that define the demand model for that particular product-store combination. For products that have a relatively high sales volume, the T-LOG data contains many data points and the demand model for that product is relatively robust. For products with few data points, however, the model parameters may be based upon too little data and the resulting model parameters can be relatively inaccurate due to statistical noise in the T-LOG data. Accordingly, for new or recently introduced products with few T-LOG data entries, it is difficult to prepare robust model parameters. Furthermore, depending upon the sales volume of the product, it may take weeks or months before the T-LOG data contains sufficient information to generate robust model parameters for the recently introduced product.